Jiuyong Li

TL;DR: Wang et al. as mentioned in this paper proposed a novel in-silico method, called miRSM (miRNA Sponge Module) to infer miRNA sponge modules in breast cancer, and applied it to the breast invasive carcinoma (BRCA) dataset provided by The Cancer Genome Altas (TCGA).

TL;DR: It is shown that the proposed method is faster than most existing methods and discovers complete statistically non-redundant subgroups by the error bounds of odds ratios.

TL;DR: This paper formulate the priority driven k-anonymisation as the k-nearest neighbor (KNN) clustering problem by adding a constraint that each cluster contains at least k tuples, and develops an efficient algorithm for priority drive.

TL;DR: This paper proposes a general detection framework by combining a data mining method with a well established counterfactual reasoning framework, potential outcome model, which is efficient, and scales well with the data set size and the number of attributes.

TL;DR: A novel framework called miRsyn is presented for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data and results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level.